Solution Architect - LangGraph & Agentic AI

<p>We are looking for an experienced <strong>Solution Architect with hands-on experience designing and deploying LangGraph-based AI solutions</strong> to lead the architecture of enterprise agentic AI platforms and applications.</p><p>You will work with business and technology stakeholders to identify high-value AI opportunities and translate them into secure, scalable, and production-ready architectures.</p><p>The role combines <strong>AI architecture, enterprise integration, cloud engineering, agentic AI, security, governance, and stakeholder leadership</strong>.</p><p>You will be expected to understand LangGraph at a practical level and be able to challenge architectural decisions, guide engineering teams, and ensure that AI solutions can operate reliably at enterprise scale.</p><p><strong>Requirements</strong></p><h3>AI Solution Architecture</h3><ul><li>Lead the architecture and design of enterprise <strong>AI agent and agentic workflow solutions</strong>.</li><li>Design LangGraph-based architectures for single-agent and multi-agent applications.</li><li>Translate business requirements, processes, SLAs, security requirements, and technical constraints into solution architectures.</li><li>Evaluate architectural alternatives and document key technical decisions and trade-offs.</li><li>Define reusable architecture patterns for agentic AI solutions.</li></ul><h3>Enterprise Agent Architecture</h3><ul><li>Design architectures incorporating:</li><ul><li>LLMs</li><li>LangGraph</li><li>RAG</li><li>Enterprise data</li><li>APIs and business systems</li><li>Workflow engines</li><li>Human approval processes</li><li>Observability</li><li>Security and governance</li></ul><li>Define appropriate boundaries between AI reasoning and deterministic business logic.</li><li>Design state management, persistence, recovery, and long-running agent workflows.</li><li>Determine when to use single-agent, multi-agent, or conventional application architectures.</li></ul><h3>Cloud and Platform Architecture</h3><ul><li>Design scalable AI application architectures on <strong>AWS, Azure, or GCP</strong>.</li><li>Define compute, networking, storage, API, security, and platform requirements.</li><li>Design architectures suitable for enterprise-scale production workloads.</li><li>Evaluate cloud services and AI platform capabilities based on performance, security, scalability, and cost.</li><li>Work with platform engineering and DevOps teams to establish deployment standards.</li></ul><h3>Integration Architecture</h3><ul><li>Design integration between AI agents and enterprise applications, APIs, databases, and SaaS platforms.</li><li>Define secure mechanisms for agent tool access and business-system interactions.</li><li>Design authentication, authorisation, secrets management, and access-control approaches.</li><li>Ensure AI-driven actions are traceable, auditable, and appropriately governed.</li></ul><h3>AI Security and Governance</h3><ul><li>Establish security and governance principles for enterprise AI agents.</li><li>Address risks including:</li><ul><li>Prompt injection</li><li>Data leakage</li><li>Unauthorised tool usage</li><li>Excessive agent permissions</li><li>Inaccurate or unsafe actions</li><li>Sensitive-data exposure</li></ul><li>Define appropriate human-in-the-loop controls.</li><li>Ensure solutions comply with organisational security, privacy, regulatory, and responsible-AI requirements.</li></ul><h3>AI Evaluation and Observability</h3><ul><li>Define architecture for AI application monitoring and observability.</li><li>Establish approaches for evaluating agent accuracy, reliability, latency, cost, and task completion.</li><li>Define appropriate logging, tracing, metrics, and alerting.</li><li>Establish operational processes for monitoring and continuously improving production agents.</li></ul><h3>Stakeholder and Technical Leadership</h3><ul><li>Work directly with senior business and technology stakeholders to define AI strategies and roadmaps.</li><li>Lead architecture workshops and technical design sessions.</li><li>Communicate complex AI concepts and architectural trade-offs to technical and non-technical audiences.</li><li>Provide technical direction to AI engineers, developers, data teams, and platform engineers.</li><li>Review solution designs and ensure alignment with enterprise architecture standards.</li><li>Mentor engineering teams and promote reusable AI architecture patterns.</li></ul><p>Required Experience</p><ul><li>Significant experience in <strong>solution architecture, software architecture, AI architecture, or a related role</strong>.</li><li>Hands-on experience designing and deploying <strong>LangGraph-based AI applications or agentic workflows</strong>.</li><li>Strong understanding of LLM application architectures.</li><li>Experience with enterprise AI/ML solutions in production.</li><li>Strong understanding of RAG, tool calling, agent orchestration, and human-in-the-loop patterns.</li><li>Strong experience with at least one major cloud platform: <strong>AWS, Azure, or GCP</strong>.</li><li>Strong understanding of enterprise integration patterns and APIs.</li><li>Experience with security, governance, observability, and operational requirements for production systems.</li><li>Strong technical understanding of Python and modern software engineering practices.</li></ul><p>Desirable Experience</p><ul><li>LangChain / LangSmith</li><li>Multi-agent architectures</li><li>Enterprise RAG platforms</li><li>Vector databases</li><li>Kubernetes</li><li>Event-driven architectures</li><li>Microservices</li><li>Infrastructure as Code</li><li>CI/CD</li><li>MLOps / LLMOps</li><li>AI security</li><li>Responsible AI</li><li>Large-scale enterprise transformation</li><li>Experience working directly with senior client stakeholders</li></ul><p>Find <a href="https://www.arbeitnow.com">Jobs in Germany</a> on Arbeitnow</a>